IJACSA Vol. 16 Issue 7 (2025)

1

Enhancing Trust in Human-AI Collaboration: A Conceptual Review of Operator-AI Teamwork

Author 1: Abduljaleel Hosawi Author 2: Richard Stone

Trust is vital to collaborative work between opera-tors and AI. Yet, important elements of its nature remain to be investigated, including the dynamic process of trust formation, growth, decline, and even death between an operator and an AI. This review analyzes how the dynamic development of trust is determined by… Read full abstract & cite →

Operator-AI collaboration trust calibration trust dynamics explainability transparency trust repair mechanisms cross-cultural trust Clinical Decision Support Systems (CDSS) AI autonomy and influence ethical considerations in AI team performance AI system characteristics operator competencies contextual factors framework limitations robustness Human-AI teaming design principles human competencies predictability re-liability understandability over-reliance automation bias under-trust trust measurement trust erosion

Abstract doi.org/10.14569/IJACSA.2025.0160701 PDF

2

Designing an Empathetic Conversational Agent for Student Mental Health: A Pilot Study

Author 1: Kaichi Minami Author 2: Choi Dongeun Author 3: Panote Siriaraya Author 4: Noriaki Kuwahara

This study presents the design and evaluation of a conversational agent aimed at supporting university students' mental health. We implemented two variants of a chatbot, referred to as A1 and A2, using large language models (LLMs). A1 employed a baseline prompt reflecting a structured yet neutral counseling style, while A2… Read full abstract & cite →

Conversational agent mental health large language models prompt design empathy chatbot evaluation

Abstract doi.org/10.14569/IJACSA.2025.0160702 PDF

3

Cross-Context Evaluation of an Indoor–Outdoor AR Navigation System in a University Campus Environment

Author 1: Toma Marian-Vladut Author 2: Pascu Paul Author 3: Turcu Corneliu Octavian

This paper presents a comparative evaluation of a mobile augmented reality (AR) navigation application designed for both indoor and outdoor university environments. Building on a previously validated system for indoor guidance, the current study extends the deployment to outdoor campus spaces without relying on GPS or additional infrastructure. Using visual… Read full abstract & cite →

Augmented reality hybrid navigation spatial computing usability testing indoor-outdoor contexts ARCore higher education

Abstract doi.org/10.14569/IJACSA.2025.0160703 PDF

4

Brightness-Aware Generative Adversarial Network for Low-Light Image Enhancement

Author 1: Huafei Zhao Author 2: Mideth Abisado

Images from low-light frequently exhibit poor visibility, excessive noise, and color distortion, which substantially impair both computer vision systems and human visual perception. Although numerous enhancement techniques have been developed, producing visually appealing results with well-maintained structural details and natural color reproduction continues to pose significant challenges. To address these… Read full abstract & cite →

Low-light image enhancement generative adversarial networks U-Net PatchGAN attention mechanism deep learning

Abstract doi.org/10.14569/IJACSA.2025.0160704 PDF

5

Power-Aware Video Transmission in 5G Telemedicine: Challenges, Solutions, and Future Directions

Author 1: Qiuhong SHI Author 2: Mingjing CAO Author 3: Yun BAI

The transformation of healthcare delivery through telemedicine has been significantly accelerated by the deployment of 5G networks and the integration of Internet of Things (IoT) technologies. These advancements enable real-time video-based medical services, including remote operations, urgent care, mobile procedures, and virtual consultations. However, the energy limitations of IoT devices… Read full abstract & cite →

Telemedicine Internet of Things energy efficiency video transmission

Abstract doi.org/10.14569/IJACSA.2025.0160705 PDF

6

Evaluating Intangible Software Quality Metrics for Effective Project Management Information Systems

Author 1: Gu Xin Author 2: Rozi Nor Haizan Nor Author 3: Nur Ilyana Ismarau Tajuddin Author 4: Khairi Azhar Aziz

In modern organizational environments, project management information systems (PMIS) play an important role in ensuring project success with the user requirements, keeping overall costs within the planned budget, and delivering projects at the agreed time. Selecting a high-quality PMIS is vital for the success of project management. A software quality… Read full abstract & cite →

Web-based PMIS software quality model intangible software quality metrics software quality

Abstract doi.org/10.14569/IJACSA.2025.0160706 PDF

7

CNN-LinATFormer: Enhancing PM2.5 Prediction Through Feature Assessment and Linear Attention Mechanism

Author 1: Yuchen Zhang Author 2: Rajermani Thinakaran

Atmospheric fine particulate matter (PM2.5) poses a serious threat to public health, and its accurate prediction is crucial for environmental management and pollution control. However, existing prediction methods have difficulty in effectively capturing the complex nonlinear characteristics and multi-scale spatiotemporal dependencies of PM2.5 concentration changes. To address this challenge, this… Read full abstract & cite →

PM2.5 prediction air quality forecasting deep learning convolutional neural network linear attention mechanism channel attention feature assessment hybrid model architecture environmental monitoring spatiotemporal modeling

Abstract doi.org/10.14569/IJACSA.2025.0160707 PDF

8

Transformer Model Optimization Method for Multi-Modal Data Fusion

Author 1: Shanshan Yang Author 2: Jie peng

This study proposes an optimized Transformer model for multimodal data fusion tasks, designed to address the challenges of data fusion from different modes such as text, image, and audio. By improving data preprocessing methods, optimizing model architecture and fusion strategies, the study significantly improves the performance of the model in… Read full abstract & cite →

Transformer model multimodal data fusion model optimization attention mechanism adaptive fusion

Abstract doi.org/10.14569/IJACSA.2025.0160708 PDF

9

Emotional Analysis and Interpretation of Music Conducting Works Based on Artificial Intelligence

Author 1: He Huang Author 2: Chengcheng Zhang Author 3: Yun Liu Author 4: Liyuan Liu

Emotional expression of music conductor works is the core of music performance. Based on deep learning technology, this study puts forward an emotional analysis method of music conductor works, constructs a complete framework of audio feature extraction, emotional classification, model optimization and evaluation, selects different styles of music conductor works… Read full abstract & cite →

Artificial intelligence music conductor works emotional analysis interpretation technology

Abstract doi.org/10.14569/IJACSA.2025.0160709 PDF

10

Object Recognition in Pond Environments Using Deep Learning

Author 1: Suhaila Sari Author 2: Ng Wei Jie Author 3: Nik Shahidah Afifi Md Taujuddin Author 4: Hazli Roslan Author 5: Nabilah Ibrahim Author 6: Mohd Helmy Abd Wahab

Complicated underwater environment, such as visibility limitations and illumination conditions pose significant challenges for underwater imaging and its object recognition performance. These issues are especially critical for applications involving autonomous underwater vehicles (AUVs) or robotic systems involved in object recognition tasks during search-and-retrieval operations. Moreover, high-turbidity underwater image datasets, especially… Read full abstract & cite →

Dataset deep learning object recognition pond underwater image YOLO

Abstract doi.org/10.14569/IJACSA.2025.0160710 PDF

11

FoodSharePro: An Integrated Mobile Platform for Sustainable Food Donation and Decentralized Composting

Author 1: Jamil Abedalrahim Jamil Alsayaydeh Author 2: Rex Bacarra Author 3: Shamsul Fakhar Bin Abd Gani Author 4: Serhij Mamchenko Author 5: Safarudin Gazali Herawan

Food waste is a global issue, one-third of all food produced is lost every year. This study introduces FoodSharePro, an integrated mobile-based Waste Food Management System that connects food donation and composting. The system allows for the efficient donation of surplus edible food through a mobile app and management of… Read full abstract & cite →

Food waste management food donation platforms mobile applications traditional composting smart waste systems user engagement foodsharepro

Abstract doi.org/10.14569/IJACSA.2025.0160711 PDF

12

Benefits and Challenges of Cloud Computing System in Malaysian Public Healthcare Organizations

Author 1: Nurul Izzatty Ismail Author 2: Juhari Noor Faezah Author 3: Muhammad Syukri Abdullah Author 4: Masrina Nadia Mohd Salleh

Cloud computing has become an emerging technology in information systems (IS), which brings worldwide attention to healthcare management, including in Malaysia. Therefore, the present study aims to identify the benefits and challenges of the cloud system in the healthcare sector. The TOE framework was adopted to explain the benefits and… Read full abstract & cite →

Cloud computing technology Malaysia healthcare sector public healthcare TOE framework

Abstract doi.org/10.14569/IJACSA.2025.0160712 PDF

13

niCNN: A Novel Neuromorphic Approach to Energy-Efficient and Lightweight Human Activity Recognition on Edge Devices

Author 1: Preeti Agarwal

Recent years have seen a surge in the use of deep learning for human activity recognition (HAR) in various applications. However, running complex deep learning models on edge devices with limited resources, such as processing power, memory, and energy, is challenging. The objective of this study is to design a… Read full abstract & cite →

Neuromorphic computing human activity recognition (HAR) edge computing convolution neural network (CNN) spiking neural network (SNN) sensors

Abstract doi.org/10.14569/IJACSA.2025.0160713 PDF

14

Improved YOLOv8 Model for Enhanced Small-Sized Breast Mass Detection on Magnetic Resonance Imaging

Author 1: Feiyan Wu Author 2: Chia Yean Lim Author 3: Sau Loong Ang Author 4: Jiaxin Zheng

The early detection of breast cancer is critically important for prompt treatment and rescuing lives. However, the accuracy of small-sized breast masses’ early detection in various algorithms remains unsatisfactory, as the small-sized masses often exhibit subtle features, contain blurry boundaries, and may overlap with other parts in crowded magnetic resonance… Read full abstract & cite →

Bidirectional feature pyramid network breast cancer detection convolutional block attention module MRI object detection small-sized masses YOLOv8

Abstract doi.org/10.14569/IJACSA.2025.0160714 PDF

15

SE-Pruned ResNet-18: Balancing Accuracy and Efficiency for Object Classification on Resource-Constrained Devices

Author 1: Zeyad Farisi

Deep learning-based image object classification methods often achieve high accuracy, but with the growing demand for real-time performance on resource-constrained edge devices, existing approaches face challenges in balancing accuracy, computational complexity, and model size. To alleviate this awkward situation, we propose a novel ResNet-18 architecture that integrates the Squeeze-and-Excitation (SE)… Read full abstract & cite →

ResNet-18 squeeze-and-excitation model model pruning object classification resource-constrained devices

Abstract doi.org/10.14569/IJACSA.2025.0160715 PDF

16

Advanced AI for Liver Cancer Detection: Vision Transformers, XAI and Contrastive Learning

Author 1: B C Anil Author 2: Jayasimha S R Author 3: Samitha Khaiyum Author 4: T L Divya Author 5: Rakshitha Kiran P Author 6: Vishal C

Liver cancer detection has always stood as a significant challenge in medical diagnostics, largely due to the complexity of interpreting imaging data and the critical need for accurate yet explainable results. This study explored how recent advances in artificial intelligence, specifically Vision Transformers (ViTs), Contrastive Learning, and Explainable AI (XAI)… Read full abstract & cite →

Contrastive learning explainable AI (XAI) medical imaging AI vision transformers liver cancer detection

Abstract doi.org/10.14569/IJACSA.2025.0160716 PDF

17

A Qualitative Constructivist Framework for Assessing Knowledge Transfer in Enterprise System Projects: Insights from Expert Interviews

Author 1: Jamal M. Hussien Author 2: Riza bin Sulaiman Author 3: Ali H Hassan Author 4: Mansoor Abdulhak Author 5: Hasan Kahtan Author 6: Basit Shahzad

Effective Knowledge Transfer (KT) is widely recognized as a cornerstone of success in Enterprise System Projects (ESPs). However, despite its critical role, many ESPs continue to suffer from poor KT practices, resulting in delays, cost overruns, and suboptimal system adoption. This study aims to develop a qualitative constructivist framework for… Read full abstract & cite →

Knowledge transfer enterprise system projects KTSSAF project management constructivist methodology knowledge management IS success theory

Abstract doi.org/10.14569/IJACSA.2025.0160717 PDF

18

Investigating Space-Time Dynamics in Live Memory Forensics Using Hybrid Transformer Approaches

Author 1: Sarishma Dangi Author 2: Kamal Ghanshala Author 3: Sachin Sharma

Live memory forensics plays a critical role in digital investigations by analyzing volatile memory to detect system anomalies such as malware and unauthorized process activities. Traditional approaches often fall short in modelling the evolving nature of live memory. This study presents a novel Hybrid Space-Time Transformer Architecture combining Swin Transformer… Read full abstract & cite →

Live memory forensics swin transformer longformer transformers memory acquisition anomaly detection

Abstract doi.org/10.14569/IJACSA.2025.0160718 PDF

19

Detecting Fake News Images Using a Hybrid CNN-LSTM Architecture

Author 1: Dina R. Salem Author 2: Abdullah A. Abdullah Author 3: AbdAllah A. AlHabshy Author 4: Kamal A. ElDahshan

In today's digital world, images have become a double-edged tool in the dissemination of news; as much as they contribute to enriching honest content and communicating information effectively, they are increasingly being used to mislead the public and spread fake news. The ease of manipulating images and taking them out… Read full abstract & cite →

Fake news images machine learning deep learning cloud computing CNN LSTM

Abstract doi.org/10.14569/IJACSA.2025.0160719 PDF

20

Heuristics-Based Clustering of Internet of Vehicle Based on Effective Approximate QoS Guideline for Message Dissemination

Author 1: Tanuja Kayarga Author 2: S Ananda Kumar Author 3: Lakshmi B S Author 4: Anil Kumar B H

Internet of Things and connected world concepts are emerging foreground in all walks of life, and Internet of Vehicles (IoV) is a natural evolution of these concepts for a connected vehicular environment. Clustering is needed in the IoV network for effective management of network resources and to avoid congestion in… Read full abstract & cite →

QoS clustering meta heuristics IoV PSO steiner minimal tree

Abstract doi.org/10.14569/IJACSA.2025.0160720 PDF

21

SOM-Based Leader Selection Strategies for Cooperative Spectrum Sensing in Multi-Band Multi-User 6G CR IoT

Author 1: Mayank Kothari Author 2: Suresh Kurumbanshi

In 6G Cognitive Radio Internet of Things (CR-IoT) networks, multi-band spectrum sensing cooperatively provides access to extensive spectrum resources. The suggested learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme addresses intelligent spectrum access challenges. A cooperative strategy is introduced into a dueling deep Q network to facilitate multi-user reinforcement learning… Read full abstract & cite →

Cooperative spectrum sensing reinforcement learning k-means leader selection self-organizing map

Abstract doi.org/10.14569/IJACSA.2025.0160721 PDF

22

High-Speed Fiber-Optic Communication Performance Utilizing Fiber Bragg Grating-Based Dispersion Compensation Schemes

Author 1: Kripa Kalkala Balakrishna Author 2: Karthik Palani

Chromatic dispersion is a significant limitation in optical fiber communication, as it causes pulse broadening, which negatively impacts transmission distance and data rates, both of which are critical for meeting the high-speed demands of 5G optical networks. This study focuses on addressing chromatic dispersion in Standard Single-Mode Fiber (SSMF) systems… Read full abstract & cite →

Dispersion management chirped fiber bragg grating gaussian apodization quality factor bit error rate optical transmission system

Abstract doi.org/10.14569/IJACSA.2025.0160722 PDF

23

Bio-Inspired Metaheuristic Framework for DNA Motif Discovery Using Hybrid Cluster Based Walrus Optimization

Author 1: M. Shilpa Author 2: C. Nandini

Motifs are short, recurring sequence elements with biological significance within a set of nucleotide sequences. Motif discovery is the problem of finding these motifs. The problem of motif discovery has become an important problem in the field of Bioinformatics since, it finds its applications in Drug discovery, Environmental Health Research… Read full abstract & cite →

Motifs walrus optimization algorithm meta-heuristic algorithms k-means clustering DNA bioinformatics

Abstract doi.org/10.14569/IJACSA.2025.0160723 PDF

24

Detection of Autism Spectrum Disorder (ASD) Using Lightweight Ensemble CNN Based on Facial Images for Improved Diagnostic Accuracy

Author 1: Andi Kurniawan Nugroho Author 2: Jajang Edi Priyanto Author 3: D. S. P. Vinski

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects how people talk to each other and act. The fact that ASD is becoming more common and that diagnosing it can be difficult means that early detection is important for improving treatment outcomes. This study's goal is to use lightweight… Read full abstract & cite →

Component autism spectrum disorder (ASD) early detection ensemble convolutional neural network (CNN) facial images classification accuracy

Abstract doi.org/10.14569/IJACSA.2025.0160724 PDF

25

An Adaptive SVR-Based Framework for Multimodal Corpus Classification

Author 1: Yuhui Wang

To address the challenges associated with the dynamic growth and multimodal complexity of modern corpora, an adaptive classification framework based on Support Vector Regression (SVR) was developed. A structured corpus was first constructed, followed by the extraction of salient textual features using Term Frequency–Inverse Document Frequency (TF-IDF) metrics. To accommodate… Read full abstract & cite →

SVR adaptive corpus classification incremental learning multimodal corpus feature extraction

Abstract doi.org/10.14569/IJACSA.2025.0160725 PDF

26

Speech Emotion Recognition from Audio Data Using LSTM Model

Author 1: Md. Mahbub-Or-Rashid Author 2: Akash Kumar Nondi Author 3: Abdullah Al Sadnun Author 4: Md. Anwar Hussen Wadud Author 5: T M Amir Ul Haque Bhuiyan Author 6: Md. Saddam Hossain

The capacity to comprehend and interact with others through language is the most valuable human ability. Since emotions are crucial to communication, we are well-trained to recognize and interpret the many emotions we encounter. Contrary to popular assumption, the subjective aspect of human mood makes emotion recognition difficult for computers… Read full abstract & cite →

Emotion audio data Ryerson audio-visual database Toronto emotional speech set classification layers combine

Abstract doi.org/10.14569/IJACSA.2025.0160726 PDF

27

AI-Driven Textual Feedback Analysis in E-Training Using Enhanced RoBERTa

Author 1: Rakan Saad Alotaibi Author 2: Fahad Mazyed Alotaibi Author 3: Sameer Abdullah Nooh Author 4: Abdulaziz A. Alsulami

In corporate e-training environments, traditional metrics like course completion and quiz scores often fail to reflect actual job performance. Rich insights are embedded in unstructured textual feedback, yet they remain underutilized due to limitations in existing analytical models. This study proposes E-RoBERTa, an enhanced transformer-based model designed to predict employee… Read full abstract & cite →

Job performance prediction transformer models enhanced RoBERTa domain-adaptive pretraining (DAPT) dynamic attention scaling (DAS) natural language processing (NLP) explainable AI textual feedback analysis workforce analytics

Abstract doi.org/10.14569/IJACSA.2025.0160727 PDF

28

A Deep Learning-Based Dual-Model Framework for Real-Time Malware and Network Anomaly Detection with MITRE ATT&CK Integration

Author 1: Migara H. M. S Author 2: Sandakelum M. D. B Author 3: Maduranga D. B. W. N Author 4: Kumara D. D. K. C Author 5: Harinda Fernando Author 6: Kavinga Abeywardena

The contemporary world of high connectivity in the digital realm has presented cybersecurity with more advanced threats, such as advanced malware and network attacks, which in most cases will not be detected using traditional detection tools. Static cybersecurity tools, which are traditional, often fail to deal with dynamic and hitherto… Read full abstract & cite →

Cybersecurity malware detection generative adversarial net-works deep learning MITRE ATT&CK feedforward neural network

Abstract doi.org/10.14569/IJACSA.2025.0160728 PDF

29

Method for Maternal Health Risk Assessment with Smartwatch-Based Vital Sign Measurements

Author 1: Kohei Arai Author 2: Diva Kurnianingtyas

The risk of maternal health issues remains a particular challenge in regions with scant access to continuous antenatal care. This study proposes a smartwatch-based system for evaluating the possible risks associated with maternal health through monitoring vital signs and machine learning algorithms. Using an open-access dataset from Kaggle, the smartwatch… Read full abstract & cite →

Artificial intelligence Kaggle maternal health risk assessment IoT technology classification performance pregnancy risk level ANN RF MHRL BP

Abstract doi.org/10.14569/IJACSA.2025.0160729 PDF

30

Multi-Step Cross-Domain Aspect-Based Sentiment Generation with Error Correction Mechanism

Author 1: Ningning Mao Author 2: Xuanliang Zhu Author 3: Yadi Xu

With the rapid growth of social media and user-generated content, cross-domain aspect-level sentiment analysis has become an important research direction in sentiment computing. In this study, a cross-domain sentiment analysis method based on the T5 model is proposed. This method integrates a multi-step generative training mechanism with a correction mechanism… Read full abstract & cite →

Cross-domain aspect-based sentiment analysis multi-step generation correction mechanism domain-invariant feature learning

Abstract doi.org/10.14569/IJACSA.2025.0160730 PDF

31

Perceptual Hash Techniques for Audio Copyright Protection in Decentralized Systems

Author 1: N. Kavitha Author 2: Rashmika S J Author 3: Reshika A S

The overlap of perceptual hashing technologies with blockchain is an interesting answer to strengthen copyright protection of sound in decentralized networks. As the music society still has to endure attacks on unauthorized duplication and transformation of online content, traditional security does not hold water. Perceptual hashing bridges the gap, creating… Read full abstract & cite →

Perceptual hashing blockchain audio copyright protection

Abstract doi.org/10.14569/IJACSA.2025.0160731 PDF

32

Advancing Precision Livestock Farming: Integrating Hybrid AI, IoT, Cloud and Edge Computing for Enhanced Welfare and Efficiency

Author 1: Hakim Jebari Author 2: Siham Rekiek Author 3: Kamal Reklaoui

Poultry farming is pivotal to global food security, yet maintaining optimal environmental and operational conditions remains a challenge. Suboptimal conditions, such as high temperature and humidity, promote bacterial growth and the production of toxic gases like ammonia (NH3), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and hydrogen sulfide (H2S)… Read full abstract & cite →

Hybrid artificial intelligence edge computing cloud computing Internet of Things artificial intelligence predictive analytics smart farming smart poultry farming

Abstract doi.org/10.14569/IJACSA.2025.0160732 PDF

33

AI-Powered Skin Disease Detection Using Adaptive Particle Swarm Intelligent Optimization and Hyper-Convolutional Neural Networks

Author 1: N Annalakshmi Author 2: S Umarani

In recent medical research, skin cancer has emerged as one of the most prevalent and fatal cancers globally. Previous studies have faced challenges in detecting skin cancer early due to the complexity of identifying specific skin diseases, segmenting affected areas, and selecting relevant features. To address these limitations, this study… Read full abstract & cite →

Skin cancer image preprocessing hyper-convoluted intra-capsuled neural network (HCI-CNN) adaptive Particle Swarm Intelligent Optimization (APSIO) image classification

Abstract doi.org/10.14569/IJACSA.2025.0160733 PDF

34

Deep Learning Optimization Conception: Less Data, Less Time, More Performance

Author 1: Mohamed Amine MEDDAOUI Author 2: Moulay AMZIL Author 3: Imane KARKABA Author 4: Mohammed ERRITALI

Although Deep Learning has not made a breakthrough in terms of artificial intelligence core technology, it achieves the best worldwide performance across areas such as computer vision and natural language processing. However, it depends on large-scale datasets and enormous computational resources. This paper tackles a major issue: Can we train… Read full abstract & cite →

Deep Learning AI IoT optimization transfer learning model compression few-shot learning

Abstract doi.org/10.14569/IJACSA.2025.0160734 PDF

35

A Novel Hybrid HO-CAL Framework for Enhanced Stock Index Prediction

Author 1: Zeren Shi Author 2: Othman Ibrahim Author 3: Hanini Ilyana Che Hashim

The accurate prediction of stock indexes plays a critical role in supporting investment decisions and managing financial risks. This study proposed a novel hybrid deep learning model that integrated the strengths of Convolutional Neural Networks (CNN), the Attention mechanism, and Long Short-Term Memory (LSTM) networks to enhance the modelling of… Read full abstract & cite →

Attention mechanism CNN LSTM stock index hippopotamus optimization algorithm

Abstract doi.org/10.14569/IJACSA.2025.0160735 PDF

36

The Text Mining Model for Lecturer Performance Evaluation: A Comparative Study

Author 1: Anita Ratnasari Author 2: Vina Ayumi Author 3: Mariana Purba Author 4: Wachyu Hari Haji Author 5: Handrie Noprisson Author 6: Marissa Utami

To support the evaluation of the teaching and learning process in higher education institutions, it is necessary to develop a text mining (TM) model. The aim of this research is to compare the performance of Long Short-Term Memory using Word Embedding Text to Sequence (WETS-LSTM), WETS-BiLSTM, WETS-CNN1D, and WETS-RNN, using… Read full abstract & cite →

Text mining CNN LSTM RNN text-to-sequence

Abstract doi.org/10.14569/IJACSA.2025.0160736 PDF

37

Design and Evaluation of a Biometric IoT-Based Smart Lock System with Real-Time Monitoring and Alert Mechanisms

Author 1: Jamil Abedalrahim Jamil Alsayaydeh Author 2: Mohd Faizal Yusof Author 3: Serhij Mamchenko Author 4: Rostam Affendi Hamzah Author 5: Safarudin Gazali Herawan

A smart door lock system based on IoT is presented which uses fingerprint biometric authentication, ESP32 microcontroller and Blynk IoT platform to provide a secure, user friendly and remote controllable access control solution. The proposed architecture replaces traditional locks with a real time biometric system that gives instant feedback through… Read full abstract & cite →

Smart door lock Internet of Things (IoT) biometric authentication fingerprint sensor ESP32 microcontroller Blynk IoT platform access control cybersecurity real-time monitoring home automation OLED display False Rejection Rate (FRR) False Acceptance Rate (FAR) remote monitoring smart home automation

Abstract doi.org/10.14569/IJACSA.2025.0160737 PDF

38

Modelling Cloud Computing Adoption in the Malaysian Healthcare

Author 1: Normilah Mohd Noh Author 2: Nurhizam Safie Mohd Satar Author 3: Hasimi Sallehudin Author 4: Ibrahim Hassan Mallam Author 5: Surya Sumarni Hussein Author 6: Nur Azaliah Abu Bakar

Cloud computing is increasingly reshaping the global IT landscape, offering scalable and efficient solutions across industries, including the healthcare sector. This study investigates the determinants of cloud computing adoption in the Malaysian healthcare industry by integrating the Resource-Based View (RBV) and Technology-Organisation-Environment (TOE) frameworks. Emphasising internal organisational capabilities, the study… Read full abstract & cite →

Adoption cloud computing Malaysian healthcare partial least squares-structural equation modelling resource-based view (RBV)

Abstract doi.org/10.14569/IJACSA.2025.0160738 PDF

39

Potential Variables in Pharmaceutical Drug Prediction Research with Machine Learning Approach: A Literature Review

Author 1: Gunadi Emmanuel Author 2: Yulyani Arifin Author 3: Ilvico Sonata Author 4: Muhammad Zarlis

As a downstream component of the drug supply chain, pharmaceutical installations often face uncertainty in drug demand. Predicting pharmaceutical drugs using a machine learning approach enables the development of new variables that can enhance the performance of medicine prediction. Amidst limited data and a choice of prediction algorithms, the accuracy… Read full abstract & cite →

Drug demand machine learning pharmaceutical installations prediction potential variables

Abstract doi.org/10.14569/IJACSA.2025.0160739 PDF

40

Scalable Graph Learning with Graph Convolutional Networks and Graph Attention Networks: Addressing Class Imbalance Through Augmentation and Optimized Hyperparameter Tuning

Author 1: Chaima Ahle Touate Author 2: Rachid El Ayachi Author 3: Mohamed Biniz

In this study, we propose a graph-based node classification to address challenges such as data scarcity, class imbalance, limited access to original textual content in benchmark datasets, semantic preservation, and model generalization in node classification tasks. Beyond simple data replication, we enhanced the Cora dataset by extracting content from its… Read full abstract & cite →

Graph Convolutional Networks (GCN) Graph Attention Networks (GAT) hyperparameter tuning data augmentation PEGASUS synonym replacement optuna bayesian optimization node classification class imbalance

Abstract doi.org/10.14569/IJACSA.2025.0160740 PDF

41

Enhancing Banking Data Classification Through Hybrid L2 Regularisation and Early Stopping in Artificial Neural Networks

Author 1: Khairul Nizam Abd Halim Author 2: Abdul Syukor Mohamad Jaya Author 3: Fauziah Kasmin Author 4: Azlan Abdul Aziz

The demand for robust data-driven classification (DDC) techniques remains critical in banking applications, where accurate and efficient decision-making is paramount. Artificial Neural Networks (ANNs), particularly Multi-Layer Perceptrons (MLPs), are widely used due to their strong learning capabilities. However, their performance often depends on effective hyperparameter tuning and regularisation strategies to… Read full abstract & cite →

Artificial neural networks L2 regularisation early stopping banking classification

Abstract doi.org/10.14569/IJACSA.2025.0160741 PDF

42

Empirical Validation and Enhancement of ADiBA: A Framework for Big Data Analytics Implementation

Author 1: Norhayati Daut Author 2: Naomie Salim Author 3: Sharin Hazlin Huspi Author 4: Anazida Zainal Author 5: Chan Weng Howe Author 6: Muhammad Aliif Ahmad Author 7: Siti Zaiton Mohd Hashim Author 8: Masitah Ghazali Author 9: Mohd Adham Isa Author 10: Rashidah Kadir Author 11: Nuremira Ibrahim Author 12: Norazlina Khamis

The implementation of Big Data Analytics (BDA) in organisations requires a structured approach to ensure alignment with strategic goals and infrastructure readiness. This study presents an enhanced version of the previously published ADiBA (Accelerating Digital Transformation Through Big Data Adoption) framework that aimed at guiding organizations through critical components necessary… Read full abstract & cite →

Adoption process big data big data analytics framework framework validation expert survey content validity index thematic analysis organizational implementation digital transformation

Abstract doi.org/10.14569/IJACSA.2025.0160742 PDF

43

An In-Depth Analysis of Security Flaws in Advanced Authentication Protocols for the Internet of Medical Things

Author 1: Haewon Byeon

This study evaluates a four-factor authentication protocol designed for IoT healthcare systems, identifying several key vulnerabilities that could compromise its security. The analysis highlights risks associated with node cloning, insider threats, biometric data security, session management, and scalability. To address these vulnerabilities, the study proposes a series of enhancements, including… Read full abstract & cite →

Four-factor authentication IoT Healthcare security physical unclonable functions quantum-resistant cryptography biometric data protection

Abstract doi.org/10.14569/IJACSA.2025.0160743 PDF

44

Enhancing Patient Health Through Smart IoT Technologies in Healthcare

Author 1: Monica Bhutani Author 2: Osman Elwasila Author 3: Rajermani Thinakaran Author 4: Yonis Gulzar

Health care has been revolutionized by this rapid change in the field of the Internet of things that enable smart connected devices that provide better patient monitoring, diagnosis and treatment. IoT technologies facilitate collection of real time health data and remote patient monitoring, as well as prediction, thus improving overall… Read full abstract & cite →

IoT in Healthcare smart technologies remote patient monitoring data security AI in healthcare telemedicine healthcare analytics

Abstract doi.org/10.14569/IJACSA.2025.0160744 PDF

45

Modeling an Adaptive and Collaborative E-Learning System with Artificial Intelligence Tools

Author 1: Kawtar Zargane Author 2: Hassane Kemouss Author 3: Mohamed Khaldi

In an educational environment that is undergoing digital transformation, the need to create smarter, learner-centred learning environments is becoming increasingly urgent. This article presents a conceptual modeling of an e-learning system that integrates the adaptive and collaborative dimensions, relying on the tools of artificial intelligence (AI), which occupies a central… Read full abstract & cite →

Conceptual modeling of an online learning system Adaptive system Online collaborative learning Artificial intelligence (AI) Educational software architecture UML modeling

Abstract doi.org/10.14569/IJACSA.2025.0160745 PDF

46

Determinant Factors of Success for the Village Information System in Providing Sustainable Services and Governance

Author 1: Sutia Dwi Santika Author 2: Tuga Mauritsius

The Indonesian government is working to implement a digital transformation aimed at providing effective services and governance at both central and local levels, similar to other developing countries. A key strategy for achieving this digital government transformation at the local level involves the implementation of village information system, which are… Read full abstract & cite →

E-Government System Quality Service Quality Delone and Mclean model TAM

Abstract doi.org/10.14569/IJACSA.2025.0160746 PDF

47

Machine Learning Methods for Detecting Fake News: A Systematic Literature Review of Machine Learning Applications in Key Domains

Author 1: Nur Ida Aniza Rusli Author 2: Nur Atiqah Sia Abdullah Author 3: Fatin Nabila Abd Razak Author 4: Nor Haniza Ramli

Rapid digitisation in communication and online platform growth have transformed information dissemination and facilitated rapid access while simultaneously amplifying the spread of fake news. This widespread issue undermines public trust, destabilises political systems, and threatens economic stability. Machine learning techniques have been widely applied to fake news detection, but comparative… Read full abstract & cite →

Machine learning fake news systematic review health politics economy

Abstract doi.org/10.14569/IJACSA.2025.0160747 PDF

48

SpatialSolar-Net: A Multi-Site Collaborative Framework for Solar Power Forecasting with Adaptive Spatial Correlation Assessment

Author 1: Yiming Liu Author 2: Mugambigai Darajah Author 3: Christopher Gan

The increasing penetration of solar power generation poses significant challenges for grid integration due to its inherent variability and intermittency. Existing forecasting approaches treat individual solar installations independently, failing to leverage spatial correlations between geographically proximate sites and lacking adaptive mechanisms for varying environmental conditions. This paper presents SpatialSolar-Net, a… Read full abstract & cite →

Solar power forecasting spatial correlation graph neural networks adaptive fusion multi-site collaboration renewable energy integration extreme weather robustness grid stability

Abstract doi.org/10.14569/IJACSA.2025.0160748 PDF

49

AFL-BERT : Enhancing Minority Class Detection in Multi-Label Text Classification with Adaptive Focal Loss and BERT

Author 1: Zakia Labd Author 2: Said Bahassine Author 3: Khalid Housni

Fine-tuning transformer models like Bidirectional Encoder Representations from Transformers has enhanced text classification performance. However, class imbalance remains a challenge, causing biased predictions. This study introduces an improved training strategy using a novel Adaptive Focal Loss with dynamically adjusted γ based on class frequencies. Unlike static γ values, this method… Read full abstract & cite →

Adaptive focal loss BERT imbalanced text classification multilabel text classification

Abstract doi.org/10.14569/IJACSA.2025.0160749 PDF

50

Attention Aware Dual-Path Autoencoder with Asymmetric Loss for Recognition in Complex Scenes

Author 1: Hashim Rosli Author 2: Rozniza Ali Author 3: Muhamad Suzuri Hitam Author 4: Ashanira Mat Deris Author 5: Noor Hafhizah Abd Rahim

Object recognition in complex scenes is challenging due to cluttered backgrounds, overlapping objects, and degraded image quality. Another difficulty arises from sparse label presence, as most images contain only one to three active labels despite the dataset being balanced across 20 object classes. This intra-sample sparsity complicates binary classification by… Read full abstract & cite →

Component autoencoder attention aware feature fusion image enhancement multi-label classification

Abstract doi.org/10.14569/IJACSA.2025.0160750 PDF

51

Secure Data Sharing Using Blockchain Technology: A Systematic Literature Review

Author 1: Azman Azmi Author 2: Farashazillah Yahya Author 3: Nur Afrina Azman Author 4: Hazlina Jalil

Data sharing security is currently one of the crucial parts in e-government systems. Although blockchain, a type of Distributed Ledger Technology (DLT), has been increasingly applied to enhance secure data exchange, there is a significant lack of studies focusing on the specific security factors that underpin its implementation in e-government… Read full abstract & cite →

Blockchain Distributed Ledger Technology (DLT) data sharing security e-government Systematic Literature Review (SLR) PRISMA 2020

Abstract doi.org/10.14569/IJACSA.2025.0160751 PDF

52

Enhancing Firefighter PPE Compliance Through Deep Learning and Computer Vision

Author 1: Asmaa Alayed Author 2: Razan Talal Alqurashi Author 3: Samah Hamoud Alhelali Author 4: Asrar Yousef Khadawurdi Author 5: Bashayer Fayez Khan

Ensuring firefighter safety in high-risk environments requires strict adherence to Personal Protective Equipment (PPE) protocols. This study presents an automated real-time detection system for PPE using deep learning and computer vision techniques, aiming to improve PPE compliance and overall safety monitoring. The research employs advanced object detection models, specifically YOLOv10… Read full abstract & cite →

Firefighter safety Personal Protective Equipment (PPE) Object detection YOLOv10 YOLOv11 deep learning computer vision real-time detection PPE compliance AI in public safety

Abstract doi.org/10.14569/IJACSA.2025.0160752 PDF

53

Multi-Agent Deep Reinforcement Learning Algorithms for Distributed Charging Station Management

Author 1: Li Junda Author 2: Wang Tianan Author 3: Zhang Dingyi Author 4: Wu Quancai Author 5: Liu Jian

With the continued growth of the electric vehicle (EV) fleet, the issue of cross-regional coordinated scheduling for charging infrastructure has become increasingly prominent, facing challenges such as uneven resource allocation and delayed responses. Considering the complex coupling between charging stations and the power system in a smart grid environment, this… Read full abstract & cite →

Charging station scheduling cross-regional coordination multi-agent systems deep reinforcement learning Markov decision process resource optimization uncertainty response

Abstract doi.org/10.14569/IJACSA.2025.0160753 PDF

54

A Review of Federated Learning Attacks: Threat Models and Defence Strategies

Author 1: Fizlin Zakaria Author 2: Shamsul KamalAhmad Khalid

Federated Learning (FL) has emerged as a critical paradigm in privacy-preserving machine learning, enabling collaborative model training across decentralised devices without sharing raw data. While FL enhances privacy by maintaining data locality, it remains susceptible to sophisticated adversarial attacks. This review systematically analyses the FL threat landscape and introduces a… Read full abstract & cite →

Federated learning threat models defence strategies privacy-preserving AI adversarial attacks

Abstract doi.org/10.14569/IJACSA.2025.0160754 PDF

55

Intelligent Logistics Vehicle Scheduling Based on MPHIGA

Author 1: Xinxin Gao Author 2: Qing Wang

The current intelligent logistics vehicle scheduling faces challenges, including the difficulty of obtaining real-time location data and the need for manual intervention in emergencies. To address these issues, a modified multi-population hybrid genetic algorithm is proposed, along with an intelligent scheduling model constructed through the reconstruction of domain generation strategies… Read full abstract & cite →

Multi-population hybrid improved genetic algorithm domain generation algorithm logistics vehicles co-evolution scheduling management

Abstract doi.org/10.14569/IJACSA.2025.0160755 PDF

56

Fuzzy Delphi Method: A Step-by-Step Guide to Obtaining Expert Consensus on Mobile Tourism Acceptance Culture

Author 1: Syaifullah Author 2: Shamsul Arrieya Ariffin Author 3: Norhisham Mohamad Nordin

Mobile technology has developed rapidly in a short period of time, which has greatly changed the tourism sector and led to the emergence of Mobile Tourism (MT). To ensure that MT grows well and is widely used, it is important to know how people from different cultures accept it. This… Read full abstract & cite →

Cultural acceptance Fuzzy Delphi Method (FDM) hofstede's cultural dimensions mobile tourism technology acceptance model (TAM)

Abstract doi.org/10.14569/IJACSA.2025.0160756 PDF

57

Optimized Automatic Temperature and Humidity Control for Tobacco Storage Using TwinCAT and Deep Reinforcement Learning

Author 1: Zhen Liu Author 2: Jili Wang Author 3: Shihao Song Author 4: Qiang Hua

With the rapid development of the tobacco industry, precise temperature and humidity control in storage environments has become essential for maintaining tobacco leaf quality. Traditional manual control methods suffer from low efficiency and limited accuracy, failing to meet modern storage demands. This study proposes an optimized automatic control system integrating… Read full abstract & cite →

TwinCAT deep reinforcement learning tobacco storage temperature and humidity control system optimization

Abstract doi.org/10.14569/IJACSA.2025.0160757 PDF

58

Adaptive AI-Driven Enterprise Resource Planning for Scalable and Real-Time Strategic Decision Making

Author 1: Ghayth AlMahadin

Enterprise Resource Planning (ERP) systems play a critical role in managing organizational assets and operations. However, traditional ERP systems rely on static, rule-based decision-making frameworks that lack the agility and intelligence required for real-time strategic support. To address these limitations, this study proposes an Adaptive AI-Driven Enterprise Resource Planning (A2ERP)… Read full abstract & cite →

Enterprise resource planning adaptive predictive modeling real-time decision support AI-Augmented ERP ensemble learning

Abstract doi.org/10.14569/IJACSA.2025.0160758 PDF

59

Enhancing Intent Recognition for Mixed Script Queries Using Roman Transliteration

Author 1: Anu Chaudhary Author 2: Rahul Pradhan Author 3: Shashi Shekhar

Intent identification has become a difficult problem given the rising usage of multilingual and mixed-script inquiries, especially in areas where Roman transliteration is widely employed. Traditional intent detection systems suffer from the discrepancies and differences in transliterated text, which lowers their accuracy. The objective of this paper is to examine… Read full abstract & cite →

Intent identification Intent recognition mixed script inquiries machine learning deep learning model

Abstract doi.org/10.14569/IJACSA.2025.0160759 PDF

60

A Hybrid Deep Learning and Optimization Approach for Accurate Channel Estimation in 5G MIMO-OFDM Systems

Author 1: Mohammed Fakhreldin

Channel estimation plays a pivotal role in enhancing the reliability and efficiency of 5G wireless communication systems, particularly in MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing) architectures under multipath and Doppler-affected conditions. Conventional methods such as Least Squares (LS) are widely used due to their low computational… Read full abstract & cite →

Multiple-input multiple-output channel estimation orthogonal frequency division multiplexing long short-term memory pilot length

Abstract doi.org/10.14569/IJACSA.2025.0160760 PDF

61

Explainable Approach Using Semantic-Guided Alignment for Radiology Imaging Diagnosis

Author 1: Fatima Cheddi Author 2: Ahmed Habbani Author 3: Hammadi Nait-Charif

The increased success of deep learning in the radiology imaging domain has significantly advanced automated diagnosis and report generation, aiming to enhance diagnostic precision and clinical decision-making. However, existing methods often struggle to achieve detailed morphological description, resulting in reports that provide only general information without precise clinical specifics and… Read full abstract & cite →

Automated report generation explainable AI cross-modal fusion contrastive learning semantic-guided alignment

Abstract doi.org/10.14569/IJACSA.2025.0160761 PDF

62

3D Reconstruction from JPG Images

Author 1: Youssif Mohamed Mostafa Author 2: Maryam N. Al-Berry Author 3: Howida A. Shedeed

Three-dimensional (3D) reconstruction from two-dimensional (2D) images is a fundamental challenge in computer vision and photogrammetry, with applications in medical imaging, robotics, and augmented reality. This research introduces an image-based modeling pipeline designed to overcome the inherent limitations of Joint Photographic Experts Group (JPEG) images, such as lossy compression and… Read full abstract & cite →

3D reconstruction photogrammetry computer vision image-based modeling point cloud generation JPEG images

Abstract doi.org/10.14569/IJACSA.2025.0160762 PDF

63

A Focused Survey of ECG Datasets for Artificial Intelligence-Based Atrial Fibrillation Detection

Author 1: ASSALHI Imane Author 2: Bybi Abdelmajid Author 3: Oulad Hamdaoui Hanaa Author 4: Ebobisse Djene Yves Frederic Author 5: Drissi Lahssini Hilal

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and increases the risk of stroke, heart failure, and mortality. Electrocardiography (ECG) is the most important technology for AF detection because it is inexpensive, non-invasive, and provides clinically useful information. However, the variability of ECG patterns, particularly during paroxysmal AF… Read full abstract & cite →

Atrial fibrillation ECG datasets Artificial Intelligence AI-ECG dataset survey AF detection

Abstract doi.org/10.14569/IJACSA.2025.0160763 PDF

64

DLCA-CapsNet: Dual-Lane CDH Atrous CapsNet for the Detection of Plant Diseases

Author 1: Steve Okyere-Gyamfi Author 2: Michael Asante Author 3: Yaw Marfo Missah Author 4: Kwame Ofosuhene Peasah Author 5: Vivian Akoto-Adjepong

Humanity's survival, development, and existence are deeply intertwined with agriculture, the source of most of our food. Plant disease detection helps in securing food, but manual plant disease detection is error-prone and labor-intensive. Convolutional Neural Networks (CNNs) are highly effective for automated plant disease classification, but their difficulty in recognizing… Read full abstract & cite →

Color Difference Histogram (CDH) Convolutional Neural Network (CNN) atrous Convolution Capsule Neural Network plant disease detection dynamic routing AI in agriculture

Abstract doi.org/10.14569/IJACSA.2025.0160764 PDF

65

A Comparative Study of Machine Learning Techniques for AE-Based Corrosion Detection with Emphasis on Transformer Models

Author 1: Osama Shahid Ali Author 2: Lukman B A Rahim

Corrosion-induced damage poses a critical threat to the structural integrity of fluid transport pipelines, necessitating advanced detection strategies for early intervention. This study investigates the use of acoustic emission (AE) monitoring in conjunction with machine learning techniques to identify anomalies indicative of corrosion. A comprehensive analysis of supervised, unsupervised, semi-supervised… Read full abstract & cite →

Acoustic emissions transformer based models machine learning

Abstract doi.org/10.14569/IJACSA.2025.0160765 PDF

66

Smartphone-Integrated Sensor-Based DFU Risk Assessment Using CatBoost and Deep Neuro-Fuzzy Intelligence

Author 1: Jayashree J Author 2: Vijayashree J Author 3: Perepi Rajarajeswari Author 4: Saravanan S

Diabetic Foot Ulcer (DFU) is a serious and common complication of diabetes mellitus, which can lead to lower limb amputation if not identified and treated in its early stages. This study introduces an integrated and intelligent system designed for the early detection and severity classification of DFUs by combining sensor-driven… Read full abstract & cite →

Bayesian Optimization CatBoost Deep Neuro-Fuzzy Networks (DN-FN) Diabetic Foot Ulcer (DFU) prediction sensor-based risk stratification

Abstract doi.org/10.14569/IJACSA.2025.0160766 PDF

67

Decoding Sales Order Anomalies: Advanced Predictive Modeling and Discrepancy Resolution Utilizing Machine Learning Algorithms

Author 1: Amit Kumar Soni Author 2: Pooja Jain

This study examines the accuracy of order prediction and determines the grounds for order block predictions. It sets order deviation by calculating forecasted variation using R2 scores and mean absolute deviation. The blocks that are checked mainly include—business partner block, credit block, common block, and delivery block. Demand forecasts compare… Read full abstract & cite →

Block predictions credit machine learning sales data

Abstract doi.org/10.14569/IJACSA.2025.0160767 PDF

68

Home Network Attached Storage (HOMENAS) Using Raspberry Pi with Telegram Bot Notification

Author 1: Nurul Najwa Abdul Rahid @ Abdul Rashid Author 2: Syafnidar Abdul Halim Author 3: Siti Maisarah Md Zain Author 4: Nik Aiman Shafiq Nik Shukri

This paper presents the development of the Home Network Attached Storage (HOMENAS) using Raspberry Pi with a Telegram Bot Notification. Network Attached Storage (NAS) is an independent storage system connected directly to the network that can be accessed easily. NAS devices are readily available on the market nowadays. However, the… Read full abstract & cite →

Home network NAS network attached storage raspberry pi telegram bot

Abstract doi.org/10.14569/IJACSA.2025.0160768 PDF

69

Enhancing Data Management for Decision Support Systems in Indonesian Government Internal Audit: A DMBOK Approach

Author 1: Febrian Imanda Effendy Author 2: Nilo Legowo

Indonesian public institutions, including the Financial and Development Supervisory Agency (BPKP), face challenges such as fragmented standards and poor data quality, which hinder effective Decision Support Systems (DSS). This research aims to evaluate BPKP's current analytics maturity level using the TDWI Analytics Maturity Model and to formulate a Data Management… Read full abstract & cite →

Data management data management body of knowledge Indonesian government internal audit agency decision support system

Abstract doi.org/10.14569/IJACSA.2025.0160769 PDF

70

Game Theory Meets Explainable AI: An Enhanced Approach to Understanding Black Box Models Through Shapley Values

Author 1: Mouad Louhichi Author 2: Redwane Nesmaoui Author 3: Mohamed Lazaar

The increasing complexity of machine learning models necessitates robust methods for interpretability, particularly in clustering applications, where understanding group characteristics is critical. To this end, this paper introduces a novel framework that integrates cooperative game theory and explainable artificial intelligence (XAI) to enhance the interpretability of black-box clustering models. Our… Read full abstract & cite →

Cooperative game theory Explainable Artificial Intelligence (XAI) Shapley values cluster analysis interpretability feature attribution black-box models

Abstract doi.org/10.14569/IJACSA.2025.0160770 PDF

71

Anomaly Detection and Fault Diagnosis of Power Distribution Line Point Cloud Data Based on Deep Learning

Author 1: Jiangshun Yu Author 2: Poyu You Author 3: Jian Zhao Author 4: Xianzhe Long Author 5: Yuran Chen

Early and accurate fault diagnosis in power distribution systems is essential to ensure stable electricity delivery and prevent outages. This study presents a deep learning-based anomaly detection framework that analyzes 3D LiDAR point cloud data to identify structural defects in power distribution lines. Leveraging advancements in deep learning and 3D… Read full abstract & cite →

Power distribution anomaly detection point cloud deep learning fault diagnosis

Abstract doi.org/10.14569/IJACSA.2025.0160771 PDF

72

HGWWO: A Hybrid Grey Wolf–Whale Optimizer for Load Balancing in Cloud Computing Environments

Author 1: Yameng BAI Author 2: Junxia MENG Author 3: Shuai ZHAO Author 4: Ruoyu REN

This paper aims to develop an efficient and adaptive load balancing algorithm for cloud computing environments using a novel hybrid meta-heuristic approach. Effective load balancing is necessary for optimum performance and resource utilization in cloud computing systems. Most conventional meta-heuristic algorithms suffer from premature convergence and poor exploration–exploitation tradeoffs. An… Read full abstract & cite →

Cloud computing load balancing hybrid meta-heuristic grey wolf optimizer whale optimization algorithm

Abstract doi.org/10.14569/IJACSA.2025.0160772 PDF

73

Automated Bubble Detection in Contact Lenses Using a Hybrid Deep Learning Framework

Author 1: Chee Chin Lim Author 2: Yen Fook Chong Author 3: Vikneswaran Vijean Author 4: Gei Ki Tang

This study presents a hybrid deep learning approach for automated detection of bubbles in contact lenses, aiming to enhance quality assurance in the manufacturing process. A hybrid AlexNet+SVM model was developed using transfer learning, where AlexNet’s convolutional features were leveraged for binary classification (bubble vs. normal) via a Support Vector… Read full abstract & cite →

Bubble detection contact lens quality assurance deep learning transfer learning Support Vector Machine (SVM) AlexNet image pre-processing binary classification defect detection

Abstract doi.org/10.14569/IJACSA.2025.0160773 PDF

74

Integration of Grey Wolf Optimizer Algorithm with Combinatorial Testing for Test Suite Generation

Author 1: Muhamad Asyraf Anuar Author 2: Rosziati Ibrahim Author 3: Mazidah Mat Rejab Author 4: Nurezayana Zainal

Combinatorial Testing (CT) is a software testing technique designed to detect defects in complex systems by efficiently covering diverse combinations of input parameters within given time and resource constraints. A common strategy in CT is t-way testing, which ensures that all possible interactions among any t parameters are tested at… Read full abstract & cite →

Grey wolf optimizer algorithm combinatorial testing metaheuristics t-way testing

Abstract doi.org/10.14569/IJACSA.2025.0160774 PDF

75

A Multi-Level Stacking Ensemble Model Optimized by Soft Set Theory for Customer Churn Prediction

Author 1: Nurul Nadzirah Adnan Author 2: Mohd Khalid Awang

This study proposes a multi-level stacking ensemble model enhanced by Soft Set Theory to improve the accuracy and efficiency of customer churn prediction. The proposed model leverages Soft Set Theory to eliminate redundant classifiers via the analysis of the indiscernibility matrix, increasing classifier diversity and ensemble generalization. Ten base classifiers… Read full abstract & cite →

Customer churn prediction soft set theory ensemble learning stacking models telecommunications predictive analytics

Abstract doi.org/10.14569/IJACSA.2025.0160775 PDF

76

A Dual-Path Gated Attention-Based Deep Learning Model for Automated Essay Scoring Using Linguistic Features

Author 1: Qin Jie Author 2: Congling Huang

Automated Essay Scoring (AES) has become a critical tool for scaling writing assessment in modern education. However, existing AES models often struggle to effectively evaluate both the syntactic structure and semantic meaning of essays while maintaining interpretability and fairness. This study presents a novel deep learning-based model that integrates syntactic… Read full abstract & cite →

Attention mechanism deep learning essay scoring gated fusion linguistic features semantic encoding syntactic representation

Abstract doi.org/10.14569/IJACSA.2025.0160776 PDF

77

Comparative Analysis of Cybersecurity Frameworks in Educational Institutions: Towards a Tailored Security Model

Author 1: Syarif Hidayatulloh Author 2: Aedah Binti Abd. Rahman

Educational institutions face unique cybersecurity challenges due to their open culture, decentralised structures, and limited resources. While standard frameworks such as NIST, ISO/IEC 27001, and COBIT offer comprehensive guidance, their full implementation in academic settings is often impractical. This study addresses the gap by conducting a document-based comparative analysis of… Read full abstract & cite →

Cybersecurity educational institutions cybersecurity frameworks tailored security model

Abstract doi.org/10.14569/IJACSA.2025.0160777 PDF

78

The Representation Learning Ability of Self-Supervised Learning in Unlabeled Image Data

Author 1: Jinzhu Lin Author 2: Tianwei Ni

Many existing systems struggle to strike a balance between global feature discrimination and local semantic understanding, despite the growing popularity of Self-Supervised Learning (SSL) for representation learning with unlabeled image data. This study introduces a novel SSL framework—Contrastive and Contextual Self-Supervised Representation Learning (C2SRL)—which integrates contrastive learning mechanisms with auxiliary… Read full abstract & cite →

Self-supervised learning (SSL) unlabeled image data representation learning contrastive learning convolutional neural network (CNN) image classification feature embedding label-efficient learning

Abstract doi.org/10.14569/IJACSA.2025.0160778 PDF

79

AI-Driven Firewall Log Analysis: Enhancing Threat Detection with Deep Learning Techniques

Author 1: Yasmine ABOUDRAR Author 2: Khalid BOURAGBA Author 3: Mohamed OUZZIF

As cyber-attacks get increasingly sophisticated, cybersecurity threats have surged, with 430 million new malware instances identified in 2023 representing a 36% rise compared to 2020 figures in the United States.Traditional firewall defense mechanisms are increasingly restricted. Even though firewalls are the frontline defense mechanism, their reliance on preconfigured rules and… Read full abstract & cite →

AI-driven SIEM deep learning firewall log analysis threat detection false positives cybersecurity

Abstract doi.org/10.14569/IJACSA.2025.0160779 PDF

80

Automated Dried Fish Classification Using MobileNetV2 and Transfer Learning

Author 1: Rajmohan Pardeshi Author 2: Rajermani Thinakaran Author 3: Sanjay Kharat

India, the second largest fish producer globally, contributes significantly to food security, nutrition, and economic development. Dried fish is a vital component of the fisheries value chain, especially in South Asia, yet current classification methods are manual, inconsistent, and labor-intensive. This study aims to automate dried fish classification using MobileNetV2… Read full abstract & cite →

Dried fish classification MobileNetV2 transfer learning edge deployment fisheries automation

Abstract doi.org/10.14569/IJACSA.2025.0160780 PDF

81

Enhancing Portfolio Optimization with Weighted Scoring for Return Prediction Through Machine Learning and Neural Networks

Author 1: Ruili Sun Author 2: Qiongchao Xia Author 3: Shiguo Huang

Accurately predicting stock return can enhance the effectiveness of portfolio optimization models. Many previous studies typically divide machine learning algorithms and portfolio optimization into two separate stages: the first step leverages the powerful modeling capabilities of machine learning algorithms to select stocks, and the second step optimizes weights using traditional… Read full abstract & cite →

Machine learning stock return prediction portfolio optimization support vector regression transformer NASDAQ stock market a-share stock market cryptocurrency market

Abstract doi.org/10.14569/IJACSA.2025.0160781 PDF

82

CeC-SMOTE: A Clustering and Centroid-Based Adaptive Oversampling Method for Imbalanced Data

Author 1: Xiaoling Gao Author 2: Marshima Mohd Rosli Author 3: Muhammad Izzad Ramli Author 4: Nursuriati Jamil

Class imbalance is a common challenge in real-world datasets, leading standard classifiers to perform poorly on underrepresented classes. Traditional oversampling techniques, such as SMOTE and its variants, often generate synthetic samples without fully considering the local data structure, resulting in increased noise and class overlap.This study introduces CeC-SMOTE, an adaptive… Read full abstract & cite →

Imbalanced data classification synthetic oversampling k-means clustering centroid-based neighbor

Abstract doi.org/10.14569/IJACSA.2025.0160782 PDF

83

Towards Robust IoT Security: The Impact of Data Quality and Imbalanced Data on AI-Based IDS

Author 1: Hiba El Balbali Author 2: Anas Abou El Kalam

The increased number of connected devices and the rise of Big Data have revolutionized industries and triggered a surge in cyberattacks, making security a top priority. Machine learning and Deep Learning algorithms are crucial in intrusion detection and classification, enabling systems to identify and respond to threats with precision. However… Read full abstract & cite →

Machine learning intrusion detection internet of things data quality big data

Abstract doi.org/10.14569/IJACSA.2025.0160783 PDF

84

Automated Anatomical Analysis of Wood Cross Sections Using Macroscopic Images

Author 1: Khanh Nguyen-Trong Author 2: Thanh Nhan Nguyen-Thi

Wood anatomical features are crucial in forestry science, traditionally relying on manual inspection of wood cross-sections. This conventional method is time-consuming, subjective, and dependent on expert experience. Recent advancements in deep learning offer high accuracy but often operate as black-box models, lacking interpretability and struggling with out-of-distribution challenges under real-world… Read full abstract & cite →

Wood species identification wood anatomical analysis segmentation Mask R-CNN DenseNet

Abstract doi.org/10.14569/IJACSA.2025.0160784 PDF

85

Air Quality Prediction Based on VMD-CNN-BiLSTM-Attention

Author 1: Huang Xinxin Author 2: Mohd Suffian Sulaiman Author 3: Marshima Mohd Rosli

With the advancement of industrialization, air pollution has emerged as a critical global health and environmental concern. This study presents an air quality prediction model based on variational mode decomposition, a convolutional neural network, bidirectional long short-term memory, and an attention mechanism. The variational mode decomposition method is employed to… Read full abstract & cite →

Air quality prediction variational mode decomposition convolutional neural network bidirectional long short-term memory hyperparameter optimization air quality index

Abstract doi.org/10.14569/IJACSA.2025.0160785 PDF

86

Cardio-Edge: Hardware-Software Co-design Implementation of LSTM Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices

Author 1: Nousheen Akhtar Author 2: Abdul Rehman Buzdar Author 3: Jiancun Fan Author 4: Muhammad Umair Khan

Cardiac arrhythmias should be detected at an early stage so that clinical intervention can take place and continuous patient monitoring can be established in a timely manner. In this study, we present Cardio-Edge, a hardware-software co-design implementation of an LSTM-based ECG classification system optimized for real-time use on wearable devices… Read full abstract & cite →

ECG classification wearable devices discrete wavelet transform (DWT) long short-term memory (LSTM) field-programmable gate array (FPGA)

Abstract doi.org/10.14569/IJACSA.2025.0160786 PDF

87

Robust Particle Filter for Accurate WiFi-Based Indoor Positioning in the Presence of Outlier-Corrupted Sensor Data

Author 1: Mohamed Aizad Bin Mohamed Ghazali Author 2: Aroland Kiring Author 3: Lyudmila Mihaylova Author 4: Hoe Tung Yew Author 5: Seng Kheau Chung Author 6: Farrah Wong

This study presents a comprehensive evaluation of an outlier-robust particle filter (RPF) designed to improve indoor positioning accuracy in complex environments with substantial measurement noise and outliers. The RPF’s performance is benchmarked against a standard Particle Filter (PF) using both simulated and real-world datasets. Simulation results indicate that the RPF… Read full abstract & cite →

Complex environments indoor positioning measurement noise and outliers RMSE reduction robust particle filter

Abstract doi.org/10.14569/IJACSA.2025.0160787 PDF

88

Design and Analysis of Smart Lighting System for Room Environments Using Simulation Supporting Diverse Light Bulbs

Author 1: Husnul Ajra Author 2: Mazlina Abdul Majid Author 3: Md. Shohidul Islam

Recently, the demand for intelligent, energy-efficient lighting systems has increased due to rising environ-mental concerns and increasing electricity consumption in smart room environment buildings. Conventional lighting systems often operate inefficiently, using outdated bulb technologies and lacking automation, which results in substantial energy waste, especially in rooms with variable occupancy. Lighting… Read full abstract & cite →

Bulb energy light model simulation

Abstract doi.org/10.14569/IJACSA.2025.0160788 PDF

89

Deep Learning-Driven DNA Image Encryption with Optimal Chaotic Map Selection

Author 1: Sara Bentouila Author 2: Kamel Mohamed Faraoun

This research introduces an advanced image encryption framework addressing critical security limitations in existing approaches. The study focuses on developing a robust encryption methodology that overcomes arbitrary chaotic map selection and static key generation vulnerabilities. Our approach integrates three synergistic components: a systematic chaotic map evaluation protocol identifying optimal dynamic… Read full abstract & cite →

Image encryption DNA encoding chaotic map selection lorenz system deep learning convolutional neural network (CNN) security analysis VGG16 cryptographic robustness

Abstract doi.org/10.14569/IJACSA.2025.0160789 PDF

90

Older Adults and Technology Design from the HCI Perspective

Author 1: Hasan Ali Sagga Author 2: Richard Stone

Older adults are an important segment in all societies worldwide, and this category of users cannot be ignored, considering technological progress, especially in the proliferation of smartphone applications. The expected growth in this age group in the following years, specifically in some developing countries, will present interaction challenges and opportunities… Read full abstract & cite →

Older adults HCI smartphone applications human science technology design older adults challenges

Abstract doi.org/10.14569/IJACSA.2025.0160790 PDF

91

Forecast COVID-19 Epidemics by Strengthening Deep Learning Models with Time Series Analysis

Author 1: Warapree Tangseefa Author 2: Tepanata Pumpaibool Author 3: Paisit Khanarsa Author 4: Krung Sinapiromsaran

The COVID-19 pandemic has profoundly impacted economic and social structures, directly affecting individuals’ lives. Deep learning models offer the potential to forecast future long-term trends and capture the temporal dependencies present in time series data. In this study, we propose leveraging the autocorrelation function (ACF) and the partial autocorrelation function… Read full abstract & cite →

Forecasting models COVID-19 long short-term memory gated recurrent unit autocorrelation function partial autocorrelation function

Abstract doi.org/10.14569/IJACSA.2025.0160791 PDF

92

Cyber Deception Across Domains: A Comprehensive Survey of Techniques, Challenges, and Perspectives

Author 1: Amal Sayari Author 2: Slim Rekhis

Cloud environments (CE), wireless networks (WN), cyber-physical systems (CPS), industrial control systems (ICS), smart grids (SG), internet of things (IoT), internet of vehicles (IOV), and unmanned aerial vehicles (UAV), are currently popular targets for cyberattacks due to their inherent limitations and vulnerabilities. Each domain has its own attack surfaces, weaknesses… Read full abstract & cite →

Cyber defense cyber deception cloud environments wireless networks cyber-physical systems industrial control systems smart grids internet of things internet of vehicles unmanned aerial vehicles

Abstract doi.org/10.14569/IJACSA.2025.0160792 PDF

93

Gender and Age Estimation from Facial Images Based on Multi-Task and Curriculum Learning

Author 1: Toma Brezovan Author 2: Claudiu Ionut Popîrlan

This study presents a multi-task deep learning approach for predicting age and gender attributes from facial images, with the aim of obtaining a robust dual classifier. The proposed system uses the pre-trained EfficientNet-B4 model as the feature extractor of the main model and incorporates a two-branch architecture, where the output… Read full abstract & cite →

Age estimation gender classification multi-task learning curriculum learning

Abstract doi.org/10.14569/IJACSA.2025.0160793 PDF

94

Improving Cross-Patient Epilepsy Detection via EEG Decomposition into Canonical Brain Rhythms with Deep Learning

Author 1: Jose Yauri Author 2: Elinar Carrillo-Riveros Author 3: Edith Guevara-Morote Author 4: Juan Carlos Carreño-Gamarra Author 5: Karel Peralta-Sotomayor Author 6: Pelayo Quispe-Bautista

Epilepsy affects more than 50 million people world-wide, and almost 80% of them live in low-income countries with limited access to medical and public services. Beyond these challenges, epileptic patients also face other problems, such as stigma and social exclusion due the misunderstanding of epilepsy. Thus, epilepsy has become a… Read full abstract & cite →

EEG signals EEG signal decomposition canonical brain rhythms deep learning convolutional neural network transformer neural network

Abstract doi.org/10.14569/IJACSA.2025.0160794 PDF

95

Evaluating and Interpreting Pooling Techniques in Spectrogram-Based Audio Analysis Using Diverse Metrics

Author 1: Supun Bandara Author 2: Uthayasanker Thayasivam

Audio analysis is a rapidly advancing field that spans various domains, including speech, music, and environmental sound data. Using spectrograms with Convolutional Neural Networks (CNNs) enables the visualization and extraction of critical audio features by combining time-frequency representations with deep learning. Pooling plays a crucial role in this process, as… Read full abstract & cite →

Audio data analysis pooling deep learning dimensionality reduction spectrograms

Abstract doi.org/10.14569/IJACSA.2025.0160795 PDF

96

Recommendation Engine for Amazon Magazine Subscriptions

Author 1: Sushil Khairnar Author 2: Deep Bodra

Recommender systems play a crucial role in enhancing user experience and engagement on e-commerce plat-forms by suggesting relevant products based on user behavior. In the context of Amazon’s extensive catalog of over 8,000 magazines spanning more than twenty-five categories, providing personalized magazine subscription recommendations poses a significant challenge. This study… Read full abstract & cite →

Sentiment analysis topic modeling recommender system link prediction

Abstract doi.org/10.14569/IJACSA.2025.0160796 PDF

97

Empowering Accessibility: IoT-Driven Smart Buildings for Elderly and Disabled Individuals

Author 1: Zeyad Alshboul Author 2: Burhan Mahmoud Hamadneh Author 3: Turki Mahdi Alqarni Author 4: Bajes Zeyad Aljunaeidia Author 5: Methaq Khadum

This study aims to examine the attitudes of elderly and disabled individuals in Saudi Arabia toward Internet of Things (IoT)-enabled smart home technologies, with specific attention to the influence of demographic variables. The research employed a descriptive survey design, utilizing an online questionnaire distributed to a stratified random sample of… Read full abstract & cite →

IoT smart home sustainability urban development quality of life assistive technologies

Abstract doi.org/10.14569/IJACSA.2025.0160797 PDF

98

Enhancing Dendritic Cell Algorithm by Integration with Multi-Layer Perceptron for Anomaly Detection

Author 1: Yousra Abudaqqa Author 2: Zulaiha Ali Othman Author 3: Azuraliza Abu Bakar

Anomaly detection is crucial in a variety of areas, with the Dendritic Cell Algorithm (DCA) being one of the most used artificial immune systems (AIS) and introduced for binary classification of data. Both traditional and current perspectives on classification in DCA have primarily been threshold-based methods. Such approaches are limited… Read full abstract & cite →

Dendritic Cell Algorithm (DCA) anomaly threshold Multi-Layer Perceptron (MLP) anomaly detection

Abstract doi.org/10.14569/IJACSA.2025.0160798 PDF